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Sunday, July 26, 2020 | History

5 edition of Framework for quantifying uncertainty in electric ship design found in the catalog.

Framework for quantifying uncertainty in electric ship design

Isaac Porche

Framework for quantifying uncertainty in electric ship design

by Isaac Porche

  • 90 Want to read
  • 4 Currently reading

Published by RAND, National Defense Research Institute in Santa Monica, CA .
Written in English

    Subjects:
  • Ship propulsion, Electric.,
  • Naval architecture.,
  • Uncertainty.,
  • Monte Carlo method.

  • Edition Notes

    StatementIsaac Porche, Henry Willis, Martin Ruszkowski.
    SeriesDocumented briefing / Rand Corporation ;, DB-407-ONR, Documented briefing (Rand Corporation) ;, DB-407-ONR.
    ContributionsWillis, Henry., Ruszkowski, Martin., National Defense Research Institute (U.S.), United States. Office of Naval Research.
    Classifications
    LC ClassificationsVM773 .P64 2004
    The Physical Object
    Paginationix, 75 p. :
    Number of Pages75
    ID Numbers
    Open LibraryOL3350033M
    ISBN 10083303586X
    LC Control Number2004366699
    OCLC/WorldCa55086766

    The Uncertainty Framework A simple but powerful way to characterize a product when seeking to devise the right supply chain strategy is the “uncertainty framework.” This framework specifies the two key uncertainties faced by the product—demand and supply. Fisher introduced the matching of supply chain strategies to the. Uncertainty analysis can be done in two general ways: quantitatively, by trying to estimate in numerical terms the magnitude of uncertainties in the final results (and if appropriate at key stages in the analysis); and; qualitatively, by describing and/or categorising the main uncertainties inherent in the analysis.

    Presents information to create a trade-off analysis framework for use in government and commercial acquisition environments This book presents a decision management process based on decision theory and cost analysis best practices aligned with the ISO/IEC , the Systems Engineering Handbook, and the Systems Engineering Body of Knowledge.   problem” is receiving increased attention in the design community, because quantifying the uncertainty of a model and the resulting system response predictions (e.g., in the form of probabilistic prediction intervals) is essential for robust and reliable design decision making.

    International Journal of Electrical Power & Energy Systems , A triangular grid generation and optimization framework for the design of free-form gridshells. Computer-Aided Design , Design model on ship trajectory control using particle swarm optimisation. Condition: New. 6th ed. Language: English. Brand new Book. Figliola and Beasley's 6th edition of "Theory and Design for Mechanical Measurements" provides a time-tested and respected approach to the theory of engineering measurements. An emphasis on the role of statistics and uncertainty analysis in the measuring process makes this text unique.


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Framework for quantifying uncertainty in electric ship design by Isaac Porche Download PDF EPUB FB2

Get this from a library. Framework for quantifying uncertainty in electric ship design. [Isaac Porche; Henry Willis; Martin Ruszkowski; National Defense Research Institute (U.S.); United States.

Office of Naval Research.; Rand Corporation.] -- The Office of Naval Research has been sponsoring development of analytic tools for exploring the benefits of electric drive propulsion for naval vessels. Framework for Quantifying Uncertainty in Electric Ship Design.

by Isaac R. Porche III, Henry H. Willis, Martin Ruszkowski. Related Topics: Military Ships and Naval Vessels, National Security and Terrorism; Citation; EmbedCited by: 5. Framework for Quantifying Uncertainty in Electric Ship Design ISAAC PORCHE, HENRY WILLIS, MARTIN RUSZKOWSKI DBONR March Prepared for the Office of Naval Research.

The three contributions (C) of the research project are: C1 A framework for describing and quantifying changeable design alternatives, applicable to ship design as well as engineering design in.

A new integrated platform, including data analysis tools and data-driven modeling technique, is designed to serve the maritime industry by improving operational efficiency and safety. In this paper, we focus on the design of framework of uncertainty and sensitivity analysis for ship motion data in offshore by: 5.

Ship operational performance is a complex subject, not least because of the various systems and their interactions in which a ship operates; the major factors are presented in Fig. the ship level the ship design, machinery configurations and their efficiencies determine the onboard mechanical, thermal and electrical energy flows which, despite automation built in to the configuration.

RESEARCH ARTICLE /JC A framework to quantify uncertainty in simulations of oil transport in the ocean Rafael C. Gonc¸alves1, Mohamed Iskandarani1, Ashwanth Srinivasan2, W.

Carlisle Thacker3, Eric Chassignet4, and Omar M. Knio5,6 1Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA, 2Tendral LLC, Miami.

Uncertainty is ubiquitous in modern decision-making supported by quantitative modeling. While uncertainty treatment has been initially largely developed in risk or environmental assessment, it is gaining large-spread interest in many industrial fields generating knowledge and practices going beyond the classical risk versus uncertainty or epistemic versus aleatory debates.

We summarize overall uncertainty via a credible interval for the mean. Our framework is fully Bayesian, makes more effective use of the simulation budget than other Bayesian approaches in the stochastic simulation literature, and is supported with both theoretical analysis and an empirical study.

The proposed framework can both reveal uncertainty quantification in ML/DL modeling and provide references for ML approach evaluation and architecture design in discharge simulations.

It indicates uncertainty quantification is an indispensable task for a successful application of ML/DL. The focus is on quantifying uncertainty of electric motors, generators, and other key components for electric propulsion, given limited data.

RAND developed an analytic framework to facilitate the assessment of uncertainties in performance of ship component technologies and to translate these component-level uncert ainties into ship-level. Uncertainty quantification (UQ) is an important part of mathematical modeling and simulations, which quantifies the impact of parametric uncertainty on model predictions.

This paper presents an efficient approach for polynomial chaos expansion (PCE) based UQ method in biological systems. For PCE, the key step is the stochastic Galerkin (SG) projection, which yields a family of deterministic.

This paper presents a probabilistic framework to include the effects of both aleatory and epistemic uncertainty sources in coupled multidisciplinary analysis (MDA). A likelihood-based decoupling approach has been previously developed for probabilistic analysis of multidisciplinary systems, but only with aleatory uncertainty in the inputs.

Quantifying Uncertainty Scope and Field of Application QUAM Page 3 1. Scope and Field of Application This Guide gives detailed guidance for the evaluation and expression of uncertainty in quantitative chemical analysis, based on the approach taken in the ISO “Guide to the Expression of Uncertainty in Measurement” [H.2].

Uncertainty quantification (UQ) is the science of quantitative characterization and reduction of uncertainties in both computational and real world applications.

It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.

An example would be to predict the acceleration of a human body in a head-on crash with another car: even if we exactly knew. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of s: 7.

Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians.

Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science.

It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians. full nonparametric uncertainty from one component to another.

Such simpli cations are com-monly used in uncertainty-based multidisciplinary design optimization methods as a way to avoid a system-level uncertainty analysis (see e.g., [32] for a review of these methods and their engineering applications).

As design intentions are explored through the use of designerly tools such as sketching, design activity remains divergent, iterative and uncommitted. This ambiguous uncertainty facilitates design thinking and the exploration of often ill-defined design problems.

In short, there exists a unique relationship between uncertainty and design activity. A Framework for Quantifying Measurement Uncertainties and Uncertainty Propagation in HCCI/LTGC Engine Experiments In this paper, a framework for estimating experimental measurement uncertainties for a Homogenous Charge Compression Ignition (HCCI)/Low-Temperature Gasoline Combustion (LTGC) engine testing facility is presented.3 Stating Results with Uncertainty There are two common ways to state the uncertainty of a result: in terms of a ˙, like the standard deviation of the mean ˙m, or in terms of a percent or fractional uncertainty, for which we reserve the symbol (\epsilon").

The relationship between and ˙ .Furthermore, a sensitivity analysis cannot quantify the uncertainty caused by random errors in the input data [49].

For instance, the two academic deterministic energy system modeling framework.